[Lisa_teatalk] Fwd: Soutenance de thèse de Caglar Gulcehre

Michael Noukhovitch mnoukhov at gmail.com
Fri Jun 8 16:27:46 EDT 2018


Forwarding the announcement of Caglar's thesis defense next week

*1pm*

*Friday June 15 *
*AA1360*

---------- Forwarded message ---------
From: Pierre McKenzie <mckenzie at iro.umontreal.ca>
Date: Fri, Jun 8, 2018 at 3:02 PM
Subject: Soutenance de thèse de Caglar Gulcehre
To: <seminaires at iro.umontreal.ca>


SOUTENANCE DE THESE DE DOCTORAT

Département d'informatique et de recherche opérationnelle
Université de Montréal

CANDIDAT: Caglar Gulcehre

TITRE:    Learning and Time: on Using Memory and Curricula for Natural
Language Understanding

DATE:     vendredi 15 juin 2018
HEURE:    13:00
ENDROIT:  Local 1360
           Pavillon André-Aisenstadt
           Université de Montréal

RÉSUMÉ:

In this thesis, I present some of the steps that we took towards
advancing natural language understanding and learning long-term
dependencies. The goal of those advancements is to keep us on the path
to develop better artificial intelligence algorithms by using deep
learning based architectures. Deep-learning architectures have a
profound effect on various language understanding applications such as
summarization, machine translation, language modeling and image caption
generation. I will be summarizing five different papers that we have
written during my Ph.D

In our first article, we propose a novel method to utilize the abundant
amount of available monolingual data for training neural machine
translation models. We have accomplished this goal by training a long
short-term memory (LSTM) language model on a large monolingual corpus
first and then fusing the outputs or the hidden representations of the
LSTM language model with the decoder of the neural machine translation
model which is trained end to end using an attention mechanism.

In our second paper, we propose an approach to address the problem of
rare words in general for natural language processing tasks. Our
approach augments the encoder-decoder architecture with attention model
by replacing the softmax layer with our proposed pointer-softmax layer
that defines pointers to the source sentences when the decoder predicts.

In our third paper, we propose two new approaches to learn alignments in
a sequence to sequence model. Our model addresses the difficulty of
learning alignments between the source and the target context that
arises when the source context is very long.

In "Dynamic Neural Turing Machine with Soft and Hard Addressing
Schemes," we propose a new approach for augmenting neural networks with
an explicit memory mechanism. Our model achieves promising results on
question answering and algorithmic tasks.

Finally I will conclude with our "Noisy Activation Functions" paper, in
which we propose a novel activation function that makes the activations
are stochastic by injecting the noise to them.

JURY:

Président-rapporteur: Pierre McKenzie (DIRO)
Directeur de recherche: Yoshua Bengio (DIRO)
Membre du jury: Simon Julien-Lacoste (DIRO)
Examinateur externe: Christopher Manning (Stanford)

Bienvenue à tous. Welcome to all. la présentation sera en anglais.
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